Fast spectral separation method for kinetic equation with anisotropic non-stationary collision operator retaining micro-model fidelity

Fuente: arXiv
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Main Authors: Zhao, Yue, Lei, Huan
Format: Preprint
Published: 2025
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author Zhao, Yue
Lei, Huan
author_facet Zhao, Yue
Lei, Huan
contents We present a generalized, data-driven collisional operator for one-component plasmas, learned from molecular dynamics simulations, to extend the collisional kinetic model beyond the weakly coupled regime. The proposed operator features an anisotropic, non-stationary collision kernel that accounts for particle correlations typically neglected in classical Landau formulations. To enable efficient numerical evaluation, we develop a fast spectral separation method that represents the kernel as a low-rank tensor product of univariate basis functions. This formulation admits an $O(N \log N)$ algorithm via fast Fourier transforms and preserves key physical properties, including discrete conservation laws and the H-theorem, through a structure-preserving central difference discretization. Numerical experiments demonstrate that the proposed model accurately captures plasma dynamics in the moderately coupled regime beyond the standard Landau model while maintaining high computational efficiency and structure-preserving properties.
format Preprint
id arxiv_https___arxiv_org_abs_2510_15093
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Fast spectral separation method for kinetic equation with anisotropic non-stationary collision operator retaining micro-model fidelity
Zhao, Yue
Lei, Huan
Numerical Analysis
Computational Physics
Machine Learning
We present a generalized, data-driven collisional operator for one-component plasmas, learned from molecular dynamics simulations, to extend the collisional kinetic model beyond the weakly coupled regime. The proposed operator features an anisotropic, non-stationary collision kernel that accounts for particle correlations typically neglected in classical Landau formulations. To enable efficient numerical evaluation, we develop a fast spectral separation method that represents the kernel as a low-rank tensor product of univariate basis functions. This formulation admits an $O(N \log N)$ algorithm via fast Fourier transforms and preserves key physical properties, including discrete conservation laws and the H-theorem, through a structure-preserving central difference discretization. Numerical experiments demonstrate that the proposed model accurately captures plasma dynamics in the moderately coupled regime beyond the standard Landau model while maintaining high computational efficiency and structure-preserving properties.
title Fast spectral separation method for kinetic equation with anisotropic non-stationary collision operator retaining micro-model fidelity
topic Numerical Analysis
Computational Physics
Machine Learning
url https://arxiv.org/abs/2510.15093